Kenny Walter is an editor with HCPLive. Prior to joining MJH Life Sciences in 2019, he worked as a digital reporter covering nanotechnology, life sciences, material science and more with R&D Magazine. He graduated with a degree in journalism from Temple University in 2008 and began his career as a local reporter for a chain of weekly newspapers based on the Jersey shore. When not working, he enjoys going to the beach and enjoying the shore in the summer and watching North Carolina Tar Heel basketball in the winter.
Younger individuals and those with specific sickle cell genotypes led to earlier predictions for fetal risk of sickle cell disease.
Using a new sickle cell disease (SCD) predicative model, researchers have developed a way to forecast high risk pregnant women for adverse events, as well as for fetal outcomes.
A team, led by A. Kinga Malinowski, MD, Department of Obstetrics and Gynaecology, Division of Maternal-Fetal Medicine, Mount Sinai Hospital, identified the risk factors for adverse outcomes in pregnancies of women with sickle cell disease to develop better risk prediction models.
“Our present data indicate that most pregnancies in women with SCD are affected by an adverse maternal event, while an adverse fetal event is encountered in almost half,” the authors wrote. “Risk of the former can be predicted by presence of low first-trimester hemoglobin, admission-requiring VOE in the year before pregnancy, multiple transfusions before pregnancy, HbSS/HbSβ0-thalassaemia genotype and history of maternal cardiac complications.”
There is a lack of studies credibly identifying the risk factors for pregnancy-related complications in women with sickle cell disease. In addition, the current stable of interventions carry inherent risks for the patients.
The models were created from a retrospective cohort of pregnant women treated and delivered at the Mount Sinai Hospital in Toronto with sickle cell disease, with the researchers using generalized estimating equation logistic regression with clustering by woman.
Sickle cell disease was established by hemoglobin electrophoresis, with confirmation by genetic analysis when the diagnosis was unclear and patients with HbSS and HbS/β0-thalassaemia were analyzed together, as were individuals with HbSC and HbS/β+-thalassaemia.
The researchers first noted adverse maternal and fetal events during pregnancy.
Maternal events consisted of acute anemia, cardiac, pulmonary, hepatobiliary, musculoskeletal, skin, splenic, neurological or renal complications, multi-organ failure, venous thromboembolism, admission-requiring vaso-occlusive events (VOE), red cell transfusion, mortality or hypertensive disorder of pregnancy.
The researchers identified fetal events, including preterm birth, small-for-gestational-age, and perinatal mortality.
There was a total of 199 pregnancies included in the study, 71% and 45% of which resulted in adverse maternal and fetal outcomes respectively.
The best predictors of maternal risk included low first-trimester hemoglobin, admission-requiring VOE in the year prior to the pregnancy, multiple transfusions prior to pregnancy, sickle cell disease genotype, and previous cardiac complications.
They also found younger age and sickle cell genotype allowed for earlier prediction of fetal risk and adding maternal events and high lactate dehydrogenase enabled the re-assessment of fetal risk with advancing gestation.
The 2 models were well calibrated and moderately discriminative for maternal outcome (c-statistic 0·81, cross-validated value 0·79) and fetal outcome (model-F1 c-statistic 0.68, cross-validated value 0.65; model-F2 c-statistic 0.72, cross-validated value 0.68).
“The models will allow early identification of women with SCD at high risk of adverse events, permitting early targeted interventions and ongoing fetal risk re-assessment enabling intensification of surveillance and optimization of delivery timing,” the authors wrote.
The study, “Distinct maternal and fetal pregnancy outcomes in women with sickle cell disease can be predicted using routine clinical and laboratory data,” was published online in the British Journal of Haematology.